A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction

TL;DR

This paper introduces a new benchmarking paradigm and a scale- and motion-aware model for egocentric pedestrian trajectory prediction, emphasizing scenario-based evaluation and multimodal data fusion to improve prediction accuracy.

Contribution

It proposes a novel scenario-based evaluation framework, a new metric for ranking models, and a multimodal, hierarchical prediction model with auxiliary tasks for better scene understanding.

Findings

Achieves up to 40% improvement in challenging scenarios

Highlights importance of multimodal data and ego-motion modeling

Provides a comprehensive scenario-based analysis of existing models

Abstract

Predicting pedestrian behavior is one of the main challenges for intelligent driving systems. In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual information, we extract driving scenarios for a meaningful and systematic approach to identifying challenges for prediction models. In this regard, we also propose a new metric for more effective ranking within the scenario-based evaluation. We conduct extensive empirical studies of existing models on these scenarios to expose shortcomings and strengths of different approaches. The scenario-based analysis highlights the importance of using multimodal sources of information and challenges caused by inadequate modeling of ego-motion and scale of pedestrians. To this end, we propose a novel egocentric trajectory prediction model that benefits from multimodal sources of data fused in an effective and efficient step-wise hierarchical fashion and two auxiliary tasks designed to learn more robust representation of scene dynamics. We show that our approach achieves significant improvement by up to 40% in challenging scenarios compared to the past arts via empirical evaluation on common benchmark datasets.